Faculty Opinions recommendation of Effect of cognitive reserve markers on Alzheimer pathologic progression.
Bibliographic record
Abstract
Education, occupation, premorbid intelligence and brain size are surrogate markers for cognitive reserve.Whether these markers have biological influence on Alzheimer disease (AD) pathology is not known.We thus aimed to investigate the effect of cognitive reserve proxies on longitudinal change of AD biomarkers.A total of 819 participants with normal cognition (NC), mild cognitive impairment (MCI) and mild AD were enrolled in the Alzheimer's Disease Neuroimaging Initiative and followed up with repeated measures of CSF, PET and MRI biomarkers.Generalized estimating equations were employed to assess whether biomarker rates of change were modified by reserve proxies.CSF Aβ 42 decline was slower in NC participants with higher cognitive reserve indexed by education, occupation and American National Adult Reading Test (ANART).The decline of [ 18 F] fluorodeoxyglucose PET uptake was slower in AD participants with better performance on the ANART.Education, occupation and ANART did not modify the rates of MRI hippocampal atrophy in any group.These findings remained unchanged after accounting for APOE 4, longitudinal missing data and baseline cognitive performance.Higher levels of reserve markers may slow the rate of amyloid deposition before cognitive impairment and preserve glucose metabolism at the dementia stage over the course of AD pathological progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.042 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".